Association of the dietary copper intake with all-cause and cardiovascular mortality: A prospective cohort study

Background Copper (Cu) is a component that performs a crucial role in the normal function and development of the human body. Nonetheless, it is still largely unclear how Cu consumption in the diet relates to the risk for all-cause and cardiovascular disease (CVD) mortality. Methods Data from the National Health and Nutrition Examination Survey from 2001–2018 were used to conduct a prospective cohort study of individuals between the ages of 20 years and above. Regression coefficients and 95% confidence intervals for the link between dietary Cu consumption and all-cause and cardiovascular-related mortality were computed utilizing univariate and multivariate-adjusted Cox proportional hazards models. Results A total of 197.9 million non-institutionalized American citizens were represented by the NHANES’s 39,784 participants. The link between Cu in the diet and all-cause mortality was discovered to be non-linear in our restricted cubic spline regression models. When comparing the highest with the lowest quartile of Cu consumption in the diet, the weighted multivariate hazard ratios for all-cause mortality were 0.91 (0.83–0.99) for Q2, 0.88 (0.80–0.97) for Q3, and 0.86 (0.76–0.98) for Q4 (P for trend = 0.017). An identical trend was observed for cardiovascular mortality, but the association is not significant. Conclusion The most important discovery was that higher dietary Cu consumption was associated with a lower risk of all-cause mortality. This trend was also consistent with that of cardiovascular-related mortality, but the association is not significant.

collected data on the prevalence of chronic diseases in the population. Such information is a particular advantage of the NHANES program. The investigation includes examining risk factors, that is, factors in a person's lifestyle, constitution, genetics or environment that may increase the chance of developing a disease or condition, such as smoking, alcohol consumption, sexual behaviour, drug use, physical health and activity, weight and dietary intake. The details of the NHANES laboratory or medical technologist, as well as anthropometry procedures, were demonstrated in a previous study [16].

Baseline data collection
Two 24-hour dietary-recall interviews are available to all NHANES participants. The NHANES database includes both detailed information on specific foods consumed and aggregate data on nutrient consumption. For this research, we used information from the Total nutrient Intake archive: the total amount of energy and nutrients consumed from food and drink per day for each participant. Cu intake (in mg) data are available on the NHANES portal. The average Cu intake of the two 24-h recalls was used for analysis.
Baseline data were obtained through questionnaires and comprised race/ethnicity, smoking status, age, marital status, sex, income poverty ratio for households, baseline medical history based on self-reporting (e.g., CVD, diabetes, hypercholesterolemia, hypertension), and medications (antihypertensive, hypoglycemic, lipid-lowering, etc.). Body mass index (BMI) is derived from a measure of weight and height. Standard protocols for taking measurements in the lab were used. The NHANES protocol (https://wwwn.cdc.gov/nchs/nhanes/ analyticguidelines.aspx) details the steps used to investigate and compile data on clinical laboratory and procedure availability. Below are the criteria used to describe smoking status: never smoked-have a lifetime cigarette smoking count of fewer than one hundred; former smokers -have given up smoking after consuming > 100 cigarettes; current smokers-have > 100 cigarette smoking counts on various days, if not daily, during their lifetime. Meanwhile, alcohol consumption was defined based on the following criteria: current heavy drinker (�3 drinks/ day); current moderate alcohol user (�2 drinks/day and <3 drinks/day); or current mild alcohol user (not conform to the aforementioned criteria).
Participants' CVD status was determined depending on their self-diagnoses of one or more of the following 5 CVD phenotypes: stroke, congestive heart failure, myocardial infarction, angina pectoris, and coronary artery disease. Participants were classified as having CVD if they reported having at least one health problem that was self-reported as "positive", and those with CVD could fit the criteria for more than one of the above subtypes.
Finally, chronic kidney disease was described as aberrant renal function as per kidney disease: Improving Global Outcomes 2021 clinical practice guidelines [20].

Mortality
De-identified and anonymous participant data from NHANES were linked to Medicare and death records throughout the years 2001-2018 using the sequence numbers issued to each participant. Research participants' mortality was tracked from the survey's inception through December 31, 2019. All-cause mortality and mortality linked to cardiac diseases (I00-I09, I11, I13, and I20-I51), DM (E10-E14), chronic lower respiratory illness (J40-J47), malignancies (C00-C97), cerebrovascular disease (I60-I69), Alzheimer's disease (G30), and other causes were analyzed. The 10th version of the International Classification of Diseases (ICD-10) was used to establish criteria for cardiovascular-related mortality. It includes deaths due to heart diseases (I00-I09, I11, I13, and I20-I51) and cerebrovascular diseases (I60-169), and it also helps in identifying the cause of death events.

Statistical analysis
Both the mean ± SD for normally distributed and the median and interquartile range for skewed data were used for presenting continuous variables, while numbers or percentages were employed to present categorical variables. Two-sample t-tests (categorical variables), one-way analysis of variance (ANOVA) (normal distribution), and the Kruskal-Wallis H-test (skewed distribution) were implemented to examine differences (variations) among dietary Cu consumption quartiles. Each variable was checked for any missing pieces before the data was analyzed. CKD accounted for the vast majority of the incomplete details (0.00-26.0%), hence, dummy variables were used to represent absent covariates values during analyses. We employed the restricted cubic spline model to evaluate the shape of the association between Cu in the diet and death from any cause and CVDs. The knots in the 25 th , 50 th , and 75 th quartiles were chosen. In addition, Cox proportional hazard models were estimated for Cu consumption after controlling for confounding variables, and regression coefficients and 95% CIs were used to describe the results. Regression models were estimated for the total sample and corrected for confounders such as demographic, socioeconomic, behavioral, physical examination, and medical history. A P < 0.05 indicated a significance level.

Sensitivity analysis
Subgroup analyses were carried out in terms of gender (male or female), age at the beginning of the cohort (less than or more than 65 years old), and health history (hypertension and diabetes). Dietary Cu intake was estimated by a multivariate-adjusted Cox proportional hazards model, and regression coefficients and 95% Cis were employed to describe the findings. In addition, R 4.1.2 (http://www.r-project.org) was adopted for all analyses of statistical data and modified for complicated survey design and population weighting according to the survey protocol.

Results
The 39,784 people who took part in the NHANES constituted a representative sample of 197,9 million non-institutionalized American people. The participants belonged to the age group of 47.6 ± 17.0 years and were primarily composed of 52.6% women, 69.3% non-Hispanic white, 11.2% non-Hispanic black, and 7.9% Mexican Americans (Table 1). Table 1 provides an overview of the weighted baseline characteristics of the people who took part in the research. These characteristics are stratified according to the quartiles of the individuals' dietary Cu consumption. There was a significant disparity in the ages between the quartiles of dietary Cu consumption (P < 0.001), with the individuals in the 4th quartile having an average age of 46.3 ± 15.7 years, which was lower than the ages of those in the other quartiles. Additionally, the male inclination was shown in the 4th quartile, whilst the female inclination was seen in the 1st quartile of the distribution (P < 0.001). In addition, the percentage of non-Hispanic whites was rather elevated in the 3rd and 4th quartiles (P < 0.001). In the 4th quartile, there was a greater proportion of married individuals (60.6%, P < 0.001), as well as people whose household income-to-poverty ratios were relatively high (P < 0.001). The majority of individuals who met the criteria for "college or above" education level were in the 4th quartile (70.5%, P < 0.001), and the 12% of patients in the 1st quartile had a substantially greater percentage of pre-existing cardiovascular disease at the time of the study (P < 0.001).
During the follow-up time duration of 110 months, 5,753 death events were recorded, with 1,447 of those deaths attributable to CVDs and 312 of those deaths attributable to cerebrovascular illness. As per the outcomes of the restricted cubic spline regression models, we discovered that the link between the amount of Cu in one's diet and death from any causes was not linear (Fig 1). In addition, there was an L-shaped link between the amount of Cu in a person's diet and all-cause mortality. There was a non-linear association between the amount of Cu in the diet and the risk of all-cause mortality, and the risk dropped as the amount of Cu increased (adjusted R2 = 0.008, Dxy = 0.146, P for non-linearity < 0.001). Similarly, a non-linear association was recorded between an increase in dietary Cu consumption and a reduction in the risk of death from cardiovascular-related causes (adjusted R2 = 0.004, Dxy = 0.168, P for nonlinearity < 0.001; Fig 2). The connection between dietary Cu consumption and death from any causes was 0.78 in the unadjusted, weighted model (0.71, 0.86) (S1 Table). Elevated Cu intake was shown to be related to a decreased risk of all-cause mortality after accounting for lab test results, drugs, medical history, behavior, anthropometric variables, socioeconomics, and demographics (Model 5). We also found that the weighted multivariate HR values for all-cause mortality were 0.91 (0.83-0.99) for Q2, 0.88 (0.80-0.97) for Q3, and 0.86 (0.76-0.98) for Q4 (P for trend = 0.017; Table 2).
Meanwhile, the coefficient of determination for the relationship between dietary CU consumption and death due to cardiovascular-associated causes was 0.70 within the context of the weighted unadjusted model (0.60, 0.83) (S2 Table). Elevated Cu intake was shown to be related to a lower risk of death from cardiovascular-related causes after accounting for lab test results, drugs, medical history, behavior, anthropometric variables, socioeconomics, and demographics (Model 5). A comparative analysis of the lowest dietary Cu intake quartile illustrated that the weighted multivariate HRs for death from cardiovascular-related causes were 0.92 (0.78-1.08) for Q2, 0.95 (0.80-1.13) for Q3, and 0.80 (0.63-1.02) for Q4, and the association is not significant (P for trend = 0.095; Table 2).
In addition, we conducted subgroup analyses to investigate the association between dietary Cu consumption and all-cause mortality and CVD, stratifying the results according to age, gender, and previous medical conditions (S3 Table). There was no remarkable interplay between the subgroup characteristics and the association between dietary Cu consumption and all-cause mortality and CVDs.

Discussion
By combining and processing NHANES data spanning the years 2001 through 2018, this extensive prospective research investigated the possible link between dietary Cu consumption and the risk of all-cause and cardiovascular-related mortality. In this report, participants whose dietary Cu intake was in the highest quartile recorded a lower risk of death from any cause compared to those whose intake was in the lowest quartile. This trend was also consistent with that of cardiovascular-related mortality. But the association is not significant between dietary Cu consumption and mortality from CVD risk.
Some previous studies have reported the effect of dietary Cu intake on the risk of all-cause death, but have reported inconsistent results [5,6]. The previous study conducted in the Warsaw area between spring 1999 and December 31, 2003 found a higher all-cause mortality among 146 male participants in a subgroup of older men with lower Cu intake collected using three-dimensional recording methods [21]. Few studies have used dietary Cu intake data continuously, which may allow to present a non-linear relationship between Cu intake and allcause mortality and provide more fine-grained information. Using a prospective cohort design, a relatively long follow-up period, and two consecutive 24-hour dietary reviews, our current study examined the association between adjusted dietary Cu intake and all-cause mortality in the U.S. general population by relatively comprehensive adjustment for a number of potential confounders.
Cu is a crucial transition metal required for a wide variety of vital eukaryotic functions, including lipid metabolism, redox equilibrium, and others [22]. Data from dietary surveys indicate a possible link between metabolic disorders and inadequate dietary Cu intake [23-  25]. Previous studies have found that total mortality, primary vascular mortality, cancer mortality, and respiratory mortality in elderly people in the UK are predicted by several biochemical measures of oxidation-regulating nutrients [6]. Micronutrients perform fundamental functions in overall health maintenance, but like many nutrients, it must be consumed in appropriate amounts to avoid adverse health effects. We acknowledge certain limitations in our present study. First, due to the observational nature of the research, we were unable to collect information on whether or not participants changed their diet or their behavior (including their exercise or sleeping behaviors). The diagnosis of CVD based on the self-reports from participants, this may lead to discrepancies from the actual situation. Moreover, inaccuracies in reflecting long-term food choices might exist since the dietary Cu intake was calculated only at baseline. Hence, to further validate our findings and reveal more insights into our results, further research in the form of prospective studies is needed.

Conclusion
The most noteworthy result of this research was the discovery that elevated levels of Cu in the diet were linked to reduced risk of death from all causes combined. This trend was also consistent with that of cardiovascular-related mortality, but the association is not significant. Additional research is required to confirm these findings and shed light on their implications.
Supporting information S1  Table. Weighted multivariable-adjusted hazard ratios for the association between quartiles of copper and all-cause mortality and cardiovascular mortality. (DOC)